Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials

2026-01-20

AI Summary

This study addresses limitations of existing nonparametric RMT-IF methods, which assume censoring is independent of covariates and are not applicable to cluster randomized trials, thereby restricting their efficiency and generalizability. The authors propose a novel doubly robust estimator within an augmented inverse probability weighting framework, integrating phase-specific outcome regression and cluster-specific censoring models. This approach extends RMT-IF to cluster randomized settings and effectively accommodates informative cluster size and within-cluster correlation. Variance estimation is conducted via a model-agnostic jackknife procedure. Simulation studies demonstrate favorable finite-sample performance, and the method is successfully applied to two real-world randomized trials, confirming its practical utility and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Progressive multi-state survival outcomes are common in trials with recurrent or sequential events and require treatment effect estimands that remain interpretable without proportional intensity or Markov assumptions. The restricted mean time in favor of treatment (RMT-IF) extends the restricted mean survival time to ordered multi-state processes and provides such an interpretable estimand. However, existing RMT-IF methods are nonparametric, assume covariate-independent censoring for independent observations, and do not accommodate cluster-randomized trials (CRTs), limiting both efficiency and applicability. We develop a class of doubly robust estimators for RMT-IF under right censoring using an augmented inverse-probability weighting framework that combines stage-specific outcome regression with arm-specific censoring models, yielding consistency when either nuisance model is correctly specified. We further extend the framework to CRTs by formalizing both cluster-level and individual-level average RMT-IF estimands to address informative cluster size and by constructing corresponding doubly robust estimators that account for within-cluster correlation. For inference, we employ model-agnostic jackknife variance estimators in both individually randomized and cluster-randomized settings. Extensive simulation studies demonstrate finite-sample performance, and the methods are illustrated using two randomized trial examples.

Problem

• restricted mean time in favor
• doubly robust estimation
• cluster-randomized trials
• right censoring
• multi-state survival outcomes

Innovation

• doubly robust estimation
• restricted mean time in favor
• cluster-randomized trials
• augmented inverse-probability weighting
• multi-state survival